Elephant Herding Research Articles

A Hybrid Optimization-Based Artificial Neural Network Model for Wireless Power Transfer in Electric Vehicles

Electric Vehicles (EVs) are powered by a battery mounted in the vehicle, which powers the motor and drives the wheels. Most commercial EVs can be charged by plugging them into a charging station. Such conductive recharging has various drawbacks, including physical plugging of the cable, safety concerns, and charging time. Manually charging EVs might be dangerous due to the chance of an electric spark or disaster. Advances in Wireless Power Transfer (WPT) demonstrate the capacity to transfer significant amounts of electricity over short and medium-range distances. The ultimate purpose of this paper is to improve the efficacy of electric car wireless charging systems. Here, a hybrid optimization-based Artificial Neural Network (ANN) model is applied to improve the efficacy of the WPT model in EVs. To optimize the weights of the ANN classifier, a hybrid approach termed as Grasshopper-Assisted Elephant Herd Optimization (GA-EHO) method is proposed. The GA-EHO is derived through the hybridization of Elephant Herd Optimization (EHO) Algorithm and the Grasshopper Optimization Algorithm (GOA) techniques. Finally, the experimental study reveals that at 70% learning rate, the proposed ANN system achieves a minimal MSE value of 0.0528, which is lower than other current classifiers, such as SVM, LSTM, and CNN.

Pilot Tones Design for Channel Estimation Using Elephant Herding Optimization Algorithm in Massive MIMO Systems

Pilot Tones Design for Channel Estimation Using Elephant Herding Optimization Algorithm in Massive MIMO Systems

An automated approach for human-animal conflict minimisation in Assam and protection of wildlife around the Kaziranga National Park using YOLO and SENet Attention Framework

Human-animal conflict in Assam, India's north-eastern state, is rising continuously. Because it occurs year-round, it damages agricultural productivity and kills people and animals, including elephants. When a herd of wild elephants emerges from a deep forest and enters human-inhabited territory around the Kaziranga National Park (KNP) in Assam, an alert must be sounded for the neighbourhood residents and forest workers to prevent conflicts. Another concern is that many wild animals die near the KNP while crossing the national highway NH-37 which traverses the area. During floods, animals flee to the highlands for food and shelter. An automated animal identification and warning system near the KNP may reduce human-animal confrontations. This paper reports the design of a system that attempts to address the above concerns. Artificial Intelligence (AI)-based strategies are utilized to recognize wild animals from live video sequences, provide warnings to avoid encounters, and protect humans and animals. Deep learning models and YoloV5 with the SENet attention layer are used to recognize wild animals in real-time. This model is trained using a public and customized dataset of animal species. Cameras attached to the cloud-based AI system take photographs from several KNP locations to confirm the model. The model's 96% accuracy in animal photographs and videos taken day and night and in feed from contemporaneous location has shown its utility. The model also improves reliability by 1–13% over previous methods.

Association rule hiding using enhanced elephant herding optimization algorithm

Association rule hiding is an efficient solution that helps organizations to avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. Cuckoo Optimization Algorithm (COA) sanitizes the transaction database but this method has limitation due to its slow convergence and exploitation capabilities. Hence in this paper, Enhanced Elephant Herding Optimization Algorithm for Association Rule Hiding (EEHOA4ARH) is proposed for association rule hiding. In EEHOA, two core functions such as clan updating operator and separating operator are used for association rule hiding that also realizes the fast convergence and exploitation capabilities. Moreover, the searching strategy in COA4ARH for the selection of best solution is highly time consuming. To reduce the time consumption for the selection of best solution, a Crowding Distance (CD) concept is combined with EEHOA4ARH. By continuously updating the best elephant and replacing the worst elephant in the population, EEHOA4ARH-CD sanitizes the transaction database effectively. Thus the proposed EEHOA4ARH achieves the less computation time, fast convergence and better exploitation capabilities by using crowding distance. The experimental results prove the effectiveness of the proposed EEHOA4ARH–CD method in terms of hiding failure, lost rule and execution time with 44.66 s.

Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms

This study aims at predicting clay compressibility (clay compressibility) for foundation design with unprecedented reliability and safety. To achieve this goal, we set forth several scientific objectives to enhance the accuracy of clay compressibility predictions and ensure the robustness of our models. Firstly, we explored an updated dataset comprising diverse clay types, focusing on the intricate relationship between clay compressibility and essential soil properties. By incorporating particle size distribution, the ratio between liquid limit (wLL) and plasticity index (PI), and the ratio between void ratio (eLL) and wLL, our objective was to gain a comprehensive understanding of clay behavior, facilitating more accurate predictions. Secondly, we employed Artificial Neural Networks (ANNs) as our primary modeling tool. ANNs, with their capacity to discern complex patterns within data, formed the foundation of our predictive model. Our scientific objective was to harness the power of ANNs to capture subtle relationships among input variables, thereby ensuring a robust foundation for clay compressibility predictions. To further refine our predictions, our third objective was to optimize the ANN model. We utilized five distinct metaheuristic algorithms—Bird Swarm Algorithm (BSA), Dragonfly Optimization (DO), Elephant Herding Optimization (EHO), Jaya Algorithm (JA), and Whale Optimization Algorithm (WOA). Our aim was to fine-tune the ANN model's parameters using these algorithms, capitalizing on their strengths to achieve superior predictive performance. Additionally, our study aimed to pioneer the development and evaluation of hybrid models for forecasting clay compressibility. By integrating the ANN model with the metaheuristic algorithms (BSA-ANN, DO-ANN, EHO-ANN, JA-ANN, and WOA-ANN), our objective was to create hybrid frameworks that synergistically combined the strengths of both approaches. We rigorously evaluated these hybrid models, ensuring their efficacy and reliability in practical engineering applications. Crucially, we validated our models through practical engineering scenarios. For this purpose, we prepared 30 new soil samples mirroring real-world conditions. Our objective was to apply the developed models to these samples, affirming their effectiveness and reliability in actual foundation design applications. In pursuit of identifying the most accurate and reliable predictive model, our final objective focused on the BSA-ANN model. Remarkably, this model demonstrated an exceptional accuracy of 99.8%. Our objective was to establish the BSA-ANN model as the preferred choice for practical engineering applications, ensuring high reliability and safety in foundation design.

Novel Amalgamation of Bat and Elephant Herd Optimization Algorithms for Generation Mix in Renewable Active Distribution Networks

In this article, a hybrid approach is proposed that amalgamates two prominent nature-inspired, swarm-based, meta-heuristic techniques. The amalgamation is done to solve the mix generation, complex real-life optimization problems for renewable active distribution network (ADN). Some observed shortcomings of the bat algorithm (BA) are overcome by an effective introduction of elephant herding optimization (EHO) without altering its internal mechanism. The new transformations are validated by solving a real-life optimization problem of distributed generations for power loss minimization in power distribution networks. The performance of the proposed hybrid method is found to be promising when compared with some of the well-known optimizations in the literature. The proposed amalgamation has strengthened the exploration and exploitation potential of the new algorithm in a way that, their precise balance seeks the global optima. After validations, this technique is deployed to solve a multiobjective problem of optimal generation mix for renewable ADN for various test cases. Key objectives of the designed model of mix generation in ADN include annual energy losses, bus voltage profiles, and net demand variants of the networks over multiple load levels. The optimization problems model is implemented and solved for two test systems that include a benchmark 33 bus and a real-life 108 bus distribution network. The results comparison reveals that the proposed method significantly improved the system performances in terms of reduced annual energy loss by 55.95% and 44.48%, improved bus voltage profiles by 7.94% and 11.20%, and reduced net power demand fluctuations by 52.00% and 37.60% for 33 bus and 108 bus distribution network, respectively. The suggested optimization model outperforms the current existing approaches and has promising exploration and exploitation capabilities to address optimization problems in engineering.

Metaheuristic-Assisted Contextual Post-Filtering Method for Event Recommendation System

In today’s world, the web is a prominent communication channel. However, the variety of strategies available on event-based social networks (EBSNs) also makes it difficult for users to choose the events that are most relevant to their interests. In EBSNs, searching for events that better fit a user’s preferences are necessary, complex, and time consuming due to a large number of events available. Toward this end, a community-contributed data event recommender framework assists consumers in filtering daunting information and providing appropriate feedback, making EBSNs more appealing to them. A novel customized event recommendation system that uses the “multi-criteria decision-making (MCDM) approach” to rank the events is introduced in this research work. The calculation of categorical, geographical, temporal, and social factors is carried out in the proposed model, and the recommendation list is ordered using a contextual post-filtering system that includes Weight and Filter. To align the recommendation list, a new probabilistic weight model is added. To be more constructive, this model incorporates metaheuristic reasoning, which will fine-tune the probabilistic threshold value using a new hybrid algorithm. The proposed hybrid model is referred to as Beetle Swarm Hybridized Elephant Herding Algorithm (BSH-EHA), which combines the algorithms like Elephant Herding Optimization (EHO) and Beetle Swarm Optimization (BSO) Algorithm. Finally, the top recommendations will be given to the users.

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

Fecal Metagenomics Study Reveals That a Low-Fiber Diet Drives the Migration of Wild Asian Elephants in Xishuangbanna, China.

The rare northward migration of wild Asian elephants in Xishuangbanna, China, has attracted global attention. Elephant migration is a complex ecological process, and the factors driving this long-distance migration remain elusive. In this study, fresh fecal samples were collected from both captive and wild Asian elephants, along with breastfed calves residing within the Wild Elephant Valley of Xishuangbanna. Our aim was to investigate the relationship between diet, gut microbiota, and migration patterns in Asian elephants through comprehensive metagenomic sequencing analyses. Among the breastfed Asian elephant group, Bacteroidales and Escherichia emerged as the dominant bacterial taxa, while the primary carbohydrate-active enzymes (CAZymes) enriched in this group were GH2, GH20, GH92, GH97, GH38, GH23, and GH43, aligning with their dietary source, namely breast milk. The bacterial taxa enriched in captive Asian elephants (CAEs) were mainly Butyrivibrio, Treponema, and Fibrobacter, and the enriched lignocellulose-degrading enzymes mainly included GH25, GH10, GH9, and cellulase (EC 3.2.1.4). These findings are consistent with the high-fiber diet of captive elephants. In contrast, the main bacterial taxa enriched in wild Asian elephants (WAEs) were Ruminococcus and Eubacterium, and the enriched CAZymes included GH109, GH20, GH33, GH28, GH106, and GH39. The abundance of lignocellulose-degrading bacteria and CAZyme content was low in WAEs, indicating challenges in processing high-fiber foods and explaining the low-fiber diet in this group. These findings suggest that wild elephant herds migrate in search of nutritionally suitable, low-fiber food sources.

Deep learning technique for patients healthcare monitoring using IoT body based body sensors and edge servers

Architectural, cognitive, and service layers are the three components that come together to form the system as a whole. The data that is acquired by the instruments at the application layer is processed by the system that is in charge of the network. The conceptual layer, which is where edge sensors are put, is responsible for managing radio resource management and intersensor connections in order to solve the issues raised by the physical layer about increasing power consumption and increased latency. In response to the processed data provided by the logical layer, the application layer will make judgements. The key objective is to lower prices so that they are more accessible to regular people. Patients will not only be able to maintain their financial stability, but they will also have easy access to private therapy. This research presents a solution based on the Internet of Things (IoT), which will simplify the usage of a generally complicated medical device while allowing you to do it at a reasonable cost and in the comfort of your own home. The Elephant Herding Optimizations using Convolutional Neural Networks (CNNs) method is discussed here in order to differentiate between healthy and unhealthy patterns of behavior. The scoring function, also known as fuzzy logic, is used in order to arrive at a conclusion on the severity of the irregularity. In the end, tests were carried out to see how well the recommended work fared in contrast to the existing approaches in terms of specificity, recall, f1-score, and ROC curve. These metrics were examined.

SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing

Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In this research, a Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm is incorporated with the Improved Particle Swarm Optimization (IPSO) algorithm for enhancing the task scheduling behavior by utilizing parameters like load balancing and resource allocation. The conventional EHO and PSO algorithms are improved utilizing a sine cosine-based clan-updating operator and human group optimizer that improve the algorithm’s exploration and exploitation abilities and avoid being trapped in the local optima problem. The efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. The numerical investigation indicates that the SCEHO-IPSO algorithm has a minimum memory storage of 309 kb, a latency of 1510 ms, and an execution time of 612 ms on the Kafka platform, and the obtained results reveal that the SCEHO-IPSO algorithm outperformed other conventional optimization algorithms. The SCEHO-IPSO algorithm converges faster than the other algorithms in the large search spaces, and it is appropriate for large scheduling issues.

Swarm-intelligent elephant herding optimized support vector machine for predicting heart disease

Considering the significant worldwide mortality rate of cardiac disease, early and precise prognosis is essential to enhancing patient outcomes. The accuracy, effectiveness, and dependability of the heart disease prediction techniques currently in use, however, continue to encounter difficulties. Therefore, it is essential to create novel strategies that can get beyond these constraints and offer a better way to forecast cardiac disease. A novel advanced elephant herding optimised support vector machine (AEHO-SVM) technique utilized for the study in suggest method for predicting cardiovascular disease. A heart disease dataset is used to assess the efficacy of the suggested AEHO-SVM algorithm. To deal with missing values, normalise the data, and minimise dimensionality, the dataset is pre-processed. The AEHO-SVM approach aims to maximise classification accuracy while lowering the possibility of misdiagnosis by optimising the hyperplane separation and changing the support vectors iteratively. A number of metrics, like sensitivity, accuracy, f-measure, and specificity are used for assess model's performance. The experiment show that the AEHO-SVM technique works better than conventional methods and achieves good predictive accuracy for the diagnosis of heart disease. A suggested AEHO-SVM technique makes use of the collective thinking of elephant herding and its built-in optimisation skills to provide a more effective and trustworthy prediction method.

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.

A Bio-inspired Energy Efficient Dynamic Task Scheduling (BEDTS) scheme and classification for virtualization CDC

Cloud Data Centers (CDCs) have evolved into an essential computing infrastructure for businesses. These applications generate a variety of data traffic with varying needs that must be examined and processed on CDCs. Every CDC is made up of multiple servers, virtual infrastructures, and physical connections that manage the internet's informative traffic. CDCs make use of a number of critical technologies, such as virtualization and Service Level Agreements (SLA). Virtualization makes it easier to share cloud computing resources (for example, by separating a powerful Physical Machine (PM) into a series of Virtual Machines (VM)) whose power is comparatively less. Even though virtualization increases the use of PMs by establishing a set of VMs for offering customised services to satisfy the needs of end-users, it also introduces another difficulty to cloud computing: map the VMs to the appropriate PMs. This is referred to as the VM deployment problem, and it is a Nondeterministic Polynomial time problem (NP-problem). CDC task scheduling is appropriate to augment the energy efficiency and resource usage in cloud computing. In the case of real-time tasks in virtualized CDC, the Bio-Inspired Energy Efficient Dynamic Task Scheduling (BEDTS) method is proposed. The Adaptive Elephant Herding Optimization (AEHO) technique is introduced in the BEDTS algorithm for optimal selection of VMs with resource restrictions and jobs. Initially, heterogeneous jobs and VMs are identified using a previous scheduling record. The Bayes classifier and Historical Scheduling Record (HSR), which allow for the identification of both the task type and VM type, serve as the foundation for task categorization. Then, related tasks are combined and then scheduled to make the best use of the host's operational status. When compared to previous approaches, experimental results reveal that BEDTS considerably enhances the scheduling performance on the whole, attains improved CDC resource utilisation, boosts task guarantee ratio, lowers average response time, and decreases the energy usage.

Greenery change and its impact on human-elephant conflict in Sri Lanka: a model-based assessment using Sentinel-2 imagery

ABSTRACT Human-elephant conflict (HEC) is a significant conservation issue for Asian elephants (Elephas maximus) and an environmental and socioeconomic crisis in elephant range countries, including Sri Lanka. Approximately 14,897 HEC incidents were recorded in Sri Lanka between 2015 and 2021. In this study, we present a Sri Lanka-wide analysis to explore the impact of greenery change on HEC. Our sources were official government data, and land use and land cover maps developed using Sentinel-2 satellite imagery. We applied the support vector machine (SVM), random forest (RF), and object-based image analysis classifications to classify land cover into six categories. This classification scheme also considered the differences observed in Sri Lanka’s woody vegetation, consisting of forest, open forest, paddy fields, homestead gardens, and other crops. Analysis of the accuracies of the three types of classifiers confirmed that the supervised classification with two machine learning algorithms, RF and SVM, delivered a higher level of precision in land cover classification. RF was the best option, with a 97.34% overall accuracy and a 0.94 kappa coefficient, while SVM recorded a 94.68% overall accuracy and a 0.89 kappa coefficient. According to the findings, most HEC incidences were recorded in open forests (54%), while 62% were recorded within 2 km of the forest edge. Results indicated that HEC coincides with the human-occupied changed landscape adjacent to forest reservations and patches. The findings could be valuable for HEC management by identifying areas where elephants are most likely to conflict with humans, and the government may declare these as protected areas. Also, we propose an early warning system as an effective approach that helps detect and monitor elephant herds’ movement. Therefore, implementing long-term land use planning is crucial for protecting the forest and natural habitats, restoring elephant habitats, and mitigating HEC by minimizing human encroachment and promoting sustainable land use practices.

A case study on conflict intensity between humans and elephants at Teknaf Wildlife Sanctuary, Cox’s Bazar, Bangladesh

Humans’ and elephants’ conflict is considered a common phenomenon in Bangladesh due to extreme anthropogenic interferences on elephant habitats. Concurrently, the landless people were encouraged to encroach on forest land due to their financial hardship and poor and weak forest management systems. We conducted our research work through frequent field visits, site-oriented positioned research correspondents, and an adopted semistructured questionnaire. We recorded a total of 903 conflict-related cases between humans and elephants, including crop raids (N= 337), house raids (N= 200), homestead garden raids (N= 263), and accidental attacks (N= 103), across sites, during August 2018 to July 2019, in Teknaf Wildlife Sanctuary (TWS). Elephant attack rates varied significantly between traditional forest dwellers (N= 179), illegal settlers (N= 370), villagers nearest forest (N= 212), Rohingya refugees (N= 53), and traditional forest different tribes (N= 89). It also varied along a space gradient, including inside forests (N= 423), buffer zones (N= 297), and outside forest villages (N= 183). The number of conflicts possessed by elephants varied discriminately and was unparalleled among different professions such as cattle ranchers (N= 104), gardeners (N= 112), paddy cultivators (N= 236), vegetable growers (N= 158), betel-leaf growers (N= 163), and forest resources collectors (N= 130). Most conflicts between humans and elephants occurred significantly during the night (N= 592), many during the morning (N= 154), evening (N= 138), and much less during midday (N= 19). The conflict intensity was also higher on the east coast (N= 552) than on the west coast (N= 351). The seasonal variation, including summer (N= 367), monsoon (N= 274), and winter (N= 262), was also significant. The elephant herd size was also an important factor triggering the conflict in TWS. We conclude that conflict intensity between humans and elephants is driven by settlers’ location, forest gradient, the profession of forest-dependent people, their household size and income level, attack time, and season.

A Cooperative Attack Detection Framework for MANET-Iot Network Using Optimized Gradient Boosting Convolutional Neural Network

The implementation of large-scale Internet of Things (IoT) devices results in smart cities. Using standard mobile ad-hoc networks and IoT, developers establish the communication model for a smart city. The rapid growth of IoT devices based on smart cities poses different Quality of Service (QoS) and security problems. This research presents a novel Modified Elephant Herd Optimization (MEHO) method and a Gradient Boosting Convolutional Neural Network (CNN) strategy to address these issues. The cooperative attacks with varied disruption probabilities are initially assessed at the edge nodes of the IoT network. The MEHO-based Gradient Boosting CNN (MEHO-CNN) approach effectively detects cooperative attacks, ensuring the identification of malicious entities. For traditional cloud access, both bandwidth utilization as well as expected latency are minimized in edge computing. By using the IoT network, the proposed MEHO-CNN model identifies and eliminates malicious nodes. To establish the claimed trustworthy background, the legitimate accusations are based on an examination of trust-based allegations. When compared to existing methodologies, the proposed approach lowers the impact of cooperative attacks, resulting in increased throughput, reduced attack detection rates, lower packet loss ratio, lower packet delivery ratio, and other benefits.

Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.

Hybrid Optimization Based Hindi Document Summarization Using Deep Learning Technique

The proliferation of textual information today is a result of the internet's recent development, which is widely accessible to anybody, at any time. Generally speaking, several Natural Language Processing (NLP) techniques can be used to analyze the textual information that is offered on the basis of text documents. In recent years, various text summarization techniques have been implemented in English text documents but a little amount of work is carried out in Hindi text documents summarization. In this research investigation, the Coot Remora Optimization (CRO) technique based on Deep Recurrent Neural Network (DRNN) is used to summarize Hindi documents. Here, the CRO algorithm is used to train the DRNN, which is used to compute the sentence scores.The highest scored sentences are going to included in the summary. When compared to recent optimization algorithmic techniques, such as MCRMR-SSO, Graph-based_PSO, Genetic Algorithms (GA), and Political Elephant Herding Optimization (PEHO) based Deep Long Short Term Memory (DLSTM) algorithm, the developed method is shown to be superior. Additionally, three evaluation metrics such as precision, recall, f-measure are used to analyze the performance of the CRO based DRNN technique and obtained high performance.